LinkedIn Profile Score

Scoring criteria inspired by Nicole Barra’s guide on LinkedIn profile optimization.

A skill that audits your LinkedIn profile and produces a score with actionable recommendations to improve recruiter visibility and hiring chances. Analyzes your exported LinkedIn data (native CSV format) against recruiter search behavior, keyword optimization best practices, and credibility signals. Produces a 0-100 score with per-section breakdown and specific fixes ranked by impact.

How It Works

1. Export your LinkedIn data (Settings → Data Privacy → Download your data)
2. Select “Download larger data archive” and wait for the email (24-72h)
3. Unzip the downloaded file
4. Point this skill at the unzipped folder containing the CSV files
5. Receive your score, section-by-section analysis, and prioritized improvements

The skill reads LinkedIn’s native CSV export directly. Your data stays local — it is processed by the LLM in your session and is not stored or shared externally.

Installation

Claude Code

Clone this repository and register it as a skill:

git clone https://github.com/henriquesantanati/linkedin-profile-score.git

Then in Claude Code, add the skill path to your project configuration.

Claude.ai (Projects)

1. Download this repository as a ZIP
2. In your Claude Project, upload SKILL.md as a project file
3. Optionally upload references/scoring_rubric.md and references/manual_checklist.md for more detailed analysis

Other LLMs

Upload SKILL.md along with references/scoring_rubric.md and references/manual_checklist.md as context. Then provide your LinkedIn CSV export files and ask for a profile audit.

What Gets Analyzed

15 automated sections scored from your exported data:

• Headline, About section, Experience (titles, descriptions, duration), Skills, Connections, Activity, Interests, Profile Language, Location, Contact Info, Name, Education, Certifications

7 manual checklist items for things that can’t be verified from the export:

• Open to Work, Profile Photo, Privacy/Visibility, Recommendations, Featured Section, Custom URL, Cover Photo

Output

• Overall score (0-100) with letter grade
• Per-section scores (only sections needing improvement are detailed)
• Full action plan: what is wrong, what to change, and step-by-step instructions for every fix
• Keyword gap analysis woven into each recommendation (if you specify a target role)
• Manual checklist with navigation paths and instructions for items that cannot be verified from the export

See assets/sample_report.md for an example output.

Disclaimer

This tool generates recommendations based on general LinkedIn optimization best practices and recruiter search behavior patterns. These are suggestions, not rules. You know your industry, your audience, and your career better than any automated analysis can. Use your own judgment about which recommendations to apply and which to ignore.

For example, the tool might flag a term as too niche when it’s actually well-known in your field, or suggest removing something that differentiates you. Treat the output as a starting point for reflection, not a checklist to follow blindly.

Contributing

See CONTRIBUTING.md for how to submit improvements to scoring criteria, output format, or CSV compatibility.

License

MIT — see LICENSE for details.